Location filtering based on device mobility classification

ABSTRACT

In one embodiment, a method includes obtaining location information associated with a remote device, and processing the location information. Processing the location information includes determining if the remote device is mobile. The method also includes configuring a filter such that at least one parameter indicates that the remote device is mobile if the remote device is mobile, and configuring the filter such that the at least one parameter indicates that the remote device is approximately stationary if the remote device is not mobile. The filter is applied to the location information to generate a filtered location estimate which is arranged to estimate a location of the remote device.

BACKGROUND OF THE INVENTION

The present invention relates generally to networking.

Radio frequency (RF), or radio frequency identification (RFID), tags areoften added to or otherwise incorporated into devices such that thedevices may be tracked using RF signals. Due to the time-varying natureof RF signals, the estimated location of a stationary device from whichthe RF signals are received may vary with time. That is, data pertainingto the estimated location of a stationary device, e.g., an x-coordinateand/or a y-coordinate associated with the current two-dimensionallocation of the stationary device, may vary with time. As such, it maybe difficult to identify the true or actual location of a stationarydevice.

To compensate for the time-varying nature of RF signals, data pertainingto the estimated location of a stationary device may be filtered.Filtering may also be used on data pertaining to the estimated locationof a mobile device such that an actual path traversed by the mobiledevice may be tracked. However, the time-varying nature of RF signaturesstill often leads to relatively significant errors in tracking an actualpath of a mobile device even if filtering is used.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be readily understood by the following detaileddescription in conjunction with the accompanying drawings in which:

FIG. 1 is a block diagram representation of a network which includes alocation tracking system that utilizes a device mobility classificationto perform location filtering in accordance with an embodiment of thepresent invention.

FIGS. 2A and 2B are a process flow diagram which illustrates a generalmethod of classifying a device in accordance with an embodiment of thepresent invention.

FIG. 3 is a process flow diagram which illustrates a method ofprocessing position measurements associated with a tracked device thatis moving in accordance with an embodiment of the present invention.

FIG. 4 is a process flow diagram which illustrates a method of settingfilter parameters used for location filtering, e.g., step 325 of FIG. 3,in accordance with an embodiment of the present invention.

FIG. 5 is a diagrammatic representation of a Kalman filter in accordancewith an embodiment of the present invention.

DESCRIPTION OF EXAMPLE EMBODIMENTS General Overview

According to one aspect of the present invention, a method includesobtaining location information associated with a remote device, andprocessing the location information. Processing the location informationincludes determining if the remote device is mobile. The method alsoincludes configuring a filter such that at least one parameter indicatesthat the remote device is mobile if the remote device is mobile, andconfiguring the filter such that the at least one parameter indicatesthat the remote device is approximately stationary if the remote deviceis not mobile. The filter is applied to the location information togenerate a filtered location estimate which is arranged to estimate alocation of the remote device.

Description

When the location of devices is tracked, information regarding theestimated location of the devices is often provided, e.g., through awireless network, by the devices to a location tracing system. Theestimated location of the devices may be inaccurate due to measurementnoise and other factors.

Filters, e.g., Kalman filters, are often used to reduce measurementnoise of location values such as x-position and y-position values of atwo-dimensional location of a device. The effectiveness of a filter inreducing measurement noise and, hence, increasing the accuracy of ameasurement process, may be improved with the knowledge of whether adevice which is tagged with a radio frequency (RF) tag is stationary ormobile. In one embodiment, a filter used in a location-estimatingapplication may effectively be chosen based upon whether a device isstationary or mobile.

Parameters associated with a filter, as well as a filter type, that isused in conjunction with estimating the location of a device may beadjusted based upon whether the device is likely to be stationary ormobile. That is, the estimated state of a tracked device may be used toselect parameters for use with a filter that increases the accuracy withwhich a location of the tracked device may be determined. By way ofexample, for a Kalman filter, the estimated state of a tracked devicemay be used to determine a suitable measurement noise covarianceparameter and/or a process noise covariance parameter. If a device isdetermined to be stationary, a measurement noise covariance parameterand/or a process noise covariance parameter may have a first set ofvalues. If, however, a device is determined not to be stationary, then ameasurement noise covariance parameter and/or a process noise covarianceparameter may have a different set, or sets, of values.

Referring initially to FIG. 1, a network which includes a locationtracing system that uses a device mobility classification to performlocation filtering will be described in accordance with an embodiment ofthe present invention. A network 100 may be, in one embodiment, awireless network in which a location tracking system 104 and at leastone tracked device 108 communicate using RF signals. Typically, trackeddevice 108 is remote with respect to location tracking system 104.Location tracking system 104 may be configured to effectively monitor anarea 110 such that a location of tracked device 108 within area 110 mayeffectively be determined. In the described embodiment, when locationtracking system 104 tracks tracked device 108, location tracking system104 is essentially tracing an RF tag 112 embedded in, attached to, orotherwise incorporated substantially into tracked device 108.

Location tracking system 104 includes a transceiver arrangement 116configured to transmit and to receive signals that contain informationor data. An RF tag sensing arrangement 120 may be included intransceiver arrangement 116, and is generally arranged to monitor RF tag112. That is, RF tag sensing arrangement 120 may be arranged to obtaininformation relating to a location of RF tag 112.

Location tracking system 104 also includes a categorization arrangement126 that is generally arranged to process estimated location informationobtained from tracked device 108. Categorization arrangement 126includes a device mobility classification arrangement 128 which isgenerally arranged to classify, or effectively determine a state of,tracked device 108. In the described embodiment, device mobilityclassification arrangement 128 may be configured to identify whethertracked device 108 is likely mobile or stationary at a current time. Byway of example, device mobility classification arrangement 128 mayexecute an algorithm which uses estimated location information obtainedby transceiver arrangement 116 to determine whether tracked device 108is more likely to be mobile or stationary.

A filtering arrangement 124 may obtain estimated location informationfrom transceiver arrangement 116 and a device mobility classificationfrom device mobility classification arrangement 128, and effectivelyproduce filtered location information. That is, filtering arrangement124 is arranged to use a device mobility classification to produce arelatively accurate determination of a current location of trackeddevice 108. Filtering arrangement 124 may be associated with anysuitable filter including, but not limited to including, a Kalmanfilter. In general, filtering arrangement 124 may be associated with aprocessor 130.

Location tracking system 104 also includes a memory arrangement 132 thatmay store information obtained by transceiver arrangement 116,information used by filtering arrangement 124, and informationsubstantially created by device mobility classification arrangement 128.In general, memory arrangement 132 may include, but is not limited toincluding, a cache, a data store, a database, and/or any data structurewhich stores information such that the information may be retrieved.

Tracked device 108 may generally be any device with a location that isof interest to location tracking system 104. In other words, trackeddevice 108 is a device that is arranged to be substantially tracked bylocation tracking system 104. As previously mentioned, RF tag 112 iseffectively monitored when tracked device 108 is within area 110.Tracked device 108 typically includes a transceiver arrangement 114 thatallows tracked device 108 to communicate, e.g., wirelessly, withlocation tracking system 104.

With reference to FIGS. 2A and 2B, a general process of classifying atracked device, e.g., a device which has an RF tag, as either mobile orstationary will be described in accordance with an embodiment of thepresent invention. A process 201 of classifying a tracked device beginsat step 205 in which a number (N) of x-position and y-positionmeasurements are obtained for a tracked device. The x-position andy-position measurements, or x and y coordinates, may be obtained from RFsignals by a location tracking system. Such RF signals may effectivelybe processed to identify a received signal strength indication (RSSI) ora time difference of arrival (TDOA). N may generally be any suitablenumber. By way of example, N may be substantially any number that is twoor greater. The N x-position and y-position measurements may be the Nmost recent x-position and y-position measurements obtained for atracked device. In one embodiment, the N x-position and y-positionmeasurements may be obtained substantially in real-time, although the Nx-position and y-position measurements may instead be obtained from adata store or a data buffer. It should be appreciated that althoughobtaining a two-dimensional position measurement is described, athree-dimensional position measurement may instead be obtained.

Once N x-position and y-position measurements are obtained, the Nx-position and y-position measurements may be stored in a cache in step209. Then, in step 213, a line-of-best-fit (LOBF) may be generated forthe N x-position measurements in step 213. An LOBF may be generated instep 217 for the N y-position measurements.

After the LOBFs for the N x-position and y-position measurements aregenerated, an average value of the N x-position measurements isdetermined in step 221, and an average value of the N y-positionmeasurements is determined in step 225. A determination is made in step229 as to whether the N x-position measurements deviate less from theLOBF for the N x-position measurements than from the average value forthe N x-position measurements.

If it is determined that the N x-position measurements deviate less fromthe LOBF than from the average value, the indication is that the trackeddevice may be mobile, e.g., moving relative to an x-direction in atwo-dimensional space. That is, the implication is that the N x-positionmeasurements are more likely to approximate a line than an overall pointin space. As such, it is then determined in step 231 whether the LOBFcreated from the N x-position measurements are of at least a particularlength. The particular length may vary depending upon the requirementsof a particular location tracking system. If the determination is thatthe LOBF is of at least the particular length, then the tracked deviceis assumed to be mobile in step 233, and the process of classifying atracked device is completed.

Alternatively, if it is determined in step 231 that the LOBF createdfrom the N x-position measurements is not of at least a particularlength, the indication is that although the N x-position measurementsdeviate less from the LOBF than from the average value, the trackeddevice is likely not mobile at least with respect to an x-direction in atwo-dimensional space. Accordingly, process flow moves to step 237 inwhich it is determined whether the N y-position measurements deviateless from the LOBF created from the N y-position measurements than fromthe average value of the N y-position measurements. If the determinationin step 237 is that the N y-position measurements do not deviate lessfrom the LOBF than from the average value, the tracked device is assumedto be substantially stationary in step 245, and the process ofclassifying a tracked device is completed.

If, however, the determination in step 237 is that the N y-positionmeasurements do not deviate less from the LOBF than from the averagevalue, the implication is that the tracked device may be mobile at leastwith respect to a y-direction in a two-dimensional space. As such,process flow moves from step 237 to step 239 in which it is determinedwhether the LOBF created from the N y-position measurements is of atleast a particular length. It should be appreciated that the particularlength for the N y-position measurements may be the same or differentfrom the particular length for the N x-position measurements.

When it is determined that the LOBF created from the N y-positionmeasurements is of at least a particular length, the tracked device isassumed to be mobile in step 241. Once the tracked device is assumed tobe mobile, the process of classifying a tracked device is completed.Alternatively, when it is determined in step 239 that the LOBF createdfrom the N y-position measurements is not of at least the particularlength, process flow moves from step 239 to step 245 in which thetracked device is assumed to be substantially stationary.

Returning to step 229, if the determination is that the N x-positionmeasurements deviate less from the LOBF created from the N x-positionmeasurements than from the average value for the N x-positionmeasurements, the indication is that relative to an x-direction, the Nx-position measurements are more consistent with an overall point inspace than a line. Accordingly, process flow moves to step 237 and thedetermination of whether the N y-position measurements deviate less fromthe LOBF created from the N y-position measurements than from theaverage value for the N y-position measurements.

If a tracked device is identified as being mobile, the positionmeasurements associated with the tracked device may be processed suchthat a velocity and, hence, a maximum speed of the tracked device mayeffectively be determined. FIG. 3 is a process flow diagram whichillustrates a method of processing position measurements associated witha tracked device, e.g., a device which includes an RF tag, that has beenidentified as being mobile in accordance with an embodiment of thepresent invention. A method 301 of processing position measurementsassociated with a mobile tracked device begins at step 305 in which anumber (M) of x-position and y-position measurements for a trackeddevice are obtained while the time at which each measurement is obtainedis also obtained. Hence, M (x, y) position coordinates are obtained andM corresponding time measurements are also obtained.

After the M (x, y) position coordinates and M corresponding timemeasurements are obtained, changes in the (x, y) position coordinateswith respect to changes in time is estimated in step 309. The estimatesof changes in (x, y) position coordinates with respect to changes intime is stored in step 313, e.g., in a cache or a data store.

Using the stored estimates of changes in (x, y) position coordinateswith respect to changes in time, the last M speeds of the tracked devicemay be calculated in step 317. Once the last M speeds of the trackeddevice are calculated, process flow proceeds to step 321 in which theapproximately maximum speed of the tracked device is determined from thelast M speeds of the tracked device. That is, the highest speed orvelocity is identified from amount the last M speeds of the trackeddevice.

Upon identifying the maximum speed from the last M speeds of the trackeddevice, one or more filter parameters may be set based on theapproximately maximum speed in step 325. One method of setting filterparameters will be discussed below with reference to FIG. 4. Once theone or more filter parameters are set, the method of processing positionmeasurements associated with a mobile tracked device is completed.

In general, any suitable filter may be used to process locationestimates. As previously mentioned, one suitable filter may be a Kalmanfilter. FIG. 4 is a process flow diagram which illustrates a method ofsetting filter parameters used for a Kalman filter, e.g., step 325 ofFIG. 3, in accordance with an embodiment of the present invention. Aprocess 325 of setting one or more filter parameters for a Kalman filterbegins at step 405 in which a threshold speed is identified. Thethreshold speed may be set, in one embodiment, to reflect a boundaryabove which one set of parameter values is used and below which anotherset of parameter values is used. The threshold speed may be set to besubstantially any value that is identified as being suitable.

A determination is made in step 409 as to whether the maximum speed,e.g., the maximum speed selected from the most recent M speedscalculated for the tracked device, is above the threshold speed. In thedescribed embodiment, if the determination is that the maximum speed isabove the threshold speed, then the process noise covariance is set to ahigher value in step 413. The higher value may be the higher ofapproximately two possible process noise covariance values. After theprocess noise covariance is set to the higher value, a measurement noisecovariance is set to a lower value, e.g., a lower of approximately twovalues, in step 417. After the measurement noise covariance is set tothe lower value, the process of setting one or more parameters for aKalman filter is completed.

Returning to step 409, if it is determined that the maximum speed of thetracked device is not above the threshold speed, then the process noisecovariance is set to a lower value in step 421. After the process noisecovariance is set, the measurement noise covariance is set in step 425to a higher value. In one embodiment, the higher value is approximatelythe same value of the measurement noise covariance for a stationarytracked device. Once the measurement noise covariance is set to a highervalue, the process of setting one or more parameters for a Kalman filteris completed.

In general, location information and mobility information for a trackeddevice are provided to a Kalman filter which uses the information toeffectively generate filtered location information. If the mobilityinformation indicates that a tracked device is mobile or otherwiselikely to be moving, speed information for the tracked device is alsoprovided to the Kalman filter, as discussed above. FIG. 5 is adiagrammatic representation of a Kalman filter in accordance with anembodiment of the present invention. As will be appreciated by thoseskilled in the art, a Kalman filter 524, which may be implemented ashardware and/or software logic embodied on a tangible media and mayeffectively be executed by a computing system, utilizes variousparameters to filter data such that a mean of a squared error issubstantially minimized. Parameters used by Kalman filter 524 mayinclude, but are not limited to including, a measurement noisecovariance parameter 536 and a process noise covariance parameter 540.

Kalman filter 524 is configured to obtain raw location information, asfor example a location estimate from an RF tag in a tracked device.Kalman filter 524 is further configured to obtain mobility informationassociated with the tracked device, and to identify or select a valuefor measurement noise covariance 536 based at least in part on whetherthe mobility information indicates that the tracked device is stationaryor moving. For example, as previously mentioned, a value for measurementnoise covariance 536 may be higher for a stationary device and lower fora mobile device. If a tracked device is mobile, Kalman filter 524 mayuse information regarding the speed of the tracked device to identify orselect a value for process noise covariance 540. For example, a valuefor process noise covariance 536 may be lower for a lower speed andhigher for a higher speed.

Using raw location information, mobility information and speedinformation, as appropriate, Kalman filter 524 may generate or otherwiseprovide filtered location information. The filtered location informationis typically a more accurate indication of a location of a trackeddevice than indicated by raw location information.

Although only a few embodiments of the present invention have beendescribed, it should be understood that the present invention may beembodied in many other specific forms without departing from the spiritor the scope of the present invention. By way of example, a locationtracking system and a tracked device may operate in a variety ofdifferent environments. In one embodiment, a location tracking systemmay be an access point in a building, and a tracked device may betracked by the location tracking system while within the building.Generally, a location tracking system may be any suitable system whichis arranged to effectively monitor an area, and to receive and processsignals obtained from a tracked device within the area.

While a device mobility classification has been described as beingassociated with a two-dimensional classification, a device mobilityclassification may also be associated with a three-dimensionalclassification. That is, the mobility of a device may be trackedrelative to two dimensions or relative to three dimensions.

Parameters used with respect to a filter such as a Kalman filter havegenerally been described as having sets of values. For example, one setof values may be used when a device is determined not to be mobile,another set of values may be used when a device is mobile and has amaximum speed below a threshold speed, and yet another set of values maybe used when a device is mobile and has a maximum speed above athreshold speed. It should be appreciated, however, that any number ofsets of values may be associated with a device that is determined to bemobile. For instance, the values for a process noise covariance and/or ameasurement noise covariance may vary as a function of a maximum speed.Hence, for each speed, specific values for process noise covarianceand/or measurement noise covariance may be set without departing fromthe spirit or the scope of the present invention.

A Kalman filter is just one example of a filter with parameters whichmay be selected based at least in part upon whether a tracked device ismoving or not moving. Any suitable filter may generally be used tofacilitate location filtering. Further, different filters may be used inlocation filtering, depending upon whether the tracked device isclassified as moving or stationary. For instance, a Kalman filter may beused to perform location filtering if a tracked device is classified asmoving, while a different filter may be used to perform locationfiltering if the tracked device is classified as stationary withoutdeparting from the spirit or the scope of the present invention.

A device has generally been described as including an RF tag. It shouldbe appreciated that in lieu of including an RF tag, a device may includesubstantially any other mechanism or logic which enables a position ofthe device to be identified. In other words, a location estimate may bedetermined using data obtained through mechanisms or means other than anRF tag.

A device for which a device mobility classification is determined maygenerally be any suitable device. For example, a device may be, but isnot limited to being, a telephone such as a cellular telephone, acomputing device such as a notebook computing device, a personal digitalassistant, a portable media player, and/or an identification (ID) tag.In general, a device mobility classification may be determined forsubstantially any device within which or on which an RF tag or similardevice may be incorporated.

The embodiments of the present invention may be implemented as hardwareand/or software logic embodied in a tangible medium that, when executed,is operable to perform the various methods and processes describedabove. That is, the logic may be embodied as physical arrangements orcomponents. For example, a filter such as a Kalman filter may includehardware logic, software logic, or a combination of both hardware andsoftware logic. The tangible medium may be substantially anycomputer-readable medium that is capable of storing logic which may beexecuted, e.g., by a computing system, to perform methods and functionsassociated with the embodiments of the present invention.

The steps associated with the methods of the present invention may varywidely. Steps may be added, removed, altered, combined, and reorderedwithout departing from the spirit of the scope of the present invention.Therefore, the present examples are to be considered as illustrative andnot restrictive, and the invention is not to be limited to the detailsgiven herein, but may be modified within the scope of the appendedclaims.

1. A method comprising: obtaining location information associated with aremote device; processing the location information using a processor,wherein processing the location information includes determining if theremote device is mobile; configuring a filter such that at least oneparameter indicates that the remote device is mobile if it is determinedthat the remote device is mobile, wherein the filter is a Kalman filter,and wherein the at least one parameter includes a process noisecovariance and a measurement noise covariance; configuring the filtersuch that the at least one parameter indicates that the remote device isapproximately stationary if it is determined that the remote device isnot mobile; and applying the filter to the location information togenerate a filtered location estimate, the filtered location estimatebeing arranged to estimate a location of the remote device, whereinconfiguring the filter such that the at least one parameter indicatesthat the remote device is mobile if it is determined that the remotedevice includes determining when an approximately maximum speed of theremote device is above a threshold speed, wherein when the maximum speedis above the threshold speed, the process noise covariance is set to afirst value and the measurement noise is set to a second value.
 2. Themethod of claim 1 wherein when the maximum speed is not above thethreshold speed, the process noise covariance is set to a third valueand the measurement noise is set to a fourth value, the third valuebeing lower than the first value, the fourth value being higher than thesecond value.
 3. The method of claim 1 wherein the location informationincludes at least one x-position measurement and at least one y-positionmeasurement, and wherein processing the location information includesdetermining a first line of best fit and a first average value for theat least one x-position measurement, and determining a second line ofbest fit and a second average value for the at least one y-positionmeasurement.
 4. A method comprising: obtaining location informationassociated with a remote device, wherein the location informationincludes at least one x-position measurement and at least one y-positionmeasurement; processing the location information using a processor,wherein processing the location information includes determining if theremote device is mobile, determining a first line of best fit and afirst average value for the at least one x-position measurement, anddetermining a second line of best fit and a second average value for theat least one y-position measurement; configuring a filter such that atleast one parameter indicates that the remote device is mobile if it isdetermined that the remote device is mobile; configuring the filter suchthat the at least one parameter indicates that the remote device isapproximately stationary if it is determined that the remote device isnot mobile; applying the filter to the location information to generatea filtered location estimate, the filtered location estimate beingarranged to estimate a location of the remote device; determining whenthe at least one x-position measurement deviates less from the firstline of best fit than from the first average value, wherein the remotedevice is mobile when the at least one x-position measurement deviatesless from the first line of bets fit than from the average value; anddetermining when the at least one y-position measurement deviates lessfrom the second line of best fit than from the second average value,wherein the remote device is mobile when the at least one y-positionmeasurement deviates less from the first line of bets fit than from theaverage value.
 5. An apparatus comprising: means for obtaining locationinformation associated with a remote device; means for processing thelocation information, wherein the means for processing the locationinformation include means for determining if the remote device ismobile; means for configuring a filter such that at least one parameterindicates that the remote device is mobile if it is determined that theremote device is mobile, wherein the filter is a Kalman filter, andwherein the at least one parameter includes a process noise covarianceand a measurement noise covariance; means for configuring the filtersuch that the at least one parameter indicates that the remote device isapproximately stationary if it is determined that the remote device isnot mobile; and means for applying the filter to the locationinformation to generate a filtered location estimate, the filteredlocation estimate being arranged to estimate a location of the remotedevice, wherein the means for configuring the filter such that the atleast one parameter indicates that the remote device is mobile if it isdetermined that the remote device include means for determining when anapproximately maximum speed of the remote device is above a thresholdspeed, wherein when the maximum speed is above the threshold speed, theprocess noise covariance is set to a first value and the measurementnoise is set to a second value.
 6. Logic encoded in one or more tangiblemedia for execution and when executed operable to: obtain locationinformation associated with a remote device; process the locationinformation, wherein the logic operable to process the locationinformation is further operable to determine if the remote device ismobile; configure a filter such that at least one parameter indicatesthat the remote device is mobile if it is determined that the remotedevice is mobile, the filter is a Kalman filter, and wherein the atleast one parameter includes a process noise covariance and ameasurement noise covariance; configure the filter such that the atleast one parameter indicates that the remote device is approximatelystationary if it is determined that the remote device is not mobile; andapply the filter to the location information to generate a filteredlocation estimate, the filtered location estimate being arranged toestimate a location of the remote device, wherein the logic operable toconfigure the filter such that the at least one parameter indicates thatthe remote device is mobile if it is determined that the remote deviceis further operable to determine when an approximately maximum speed ofthe remote device is above a threshold speed, wherein when the maximumspeed is above the threshold speed, the process noise covariance is setto a first value and the measurement noise is set to a second value. 7.The logic of claim 6 wherein when the maximum speed is not above thethreshold speed, the process noise covariance is set to a third valueand the measurement noise is set to a fourth value, the third valuebeing lower than the first value, the fourth value being higher than thesecond value.
 8. The logic of claim 6 wherein the location informationincludes at least one x-position measurement and at least one y-positionmeasurement, and wherein the logic operable to process the locationinformation is further operable to determine a first line of best fitand a first average value for the at least one x-position measurement,and to determine a second line of best fit and a second average valuefor the at least one y-position measurement.
 9. Logic encoded in one ormore tangible media for execution and when executed operable to: obtainlocation information associated with a remote device, wherein thelocation information includes at least one x-position measurement and atleast one y-position measurement; process the location information,wherein the logic operable to process the location information isfurther operable to determine if the remote device is mobile, todetermine a first line of best fit and a first average value for the atleast one x-position measurement, and to determine a second line of bestfit and a second average value for the at least one y-positionmeasurement; configure a filter such that at least one parameterindicates that the remote device is mobile if it is determined that theremote device is mobile; configure the filter such that the at least oneparameter indicates that the remote device is approximately stationaryif it is determined that the remote device is not mobile; apply thefilter to the location information to generate a filtered locationestimate, the filtered location estimate being arranged to estimate alocation of the remote device; determine when the at least onex-position measurement deviates less from the first line of best fitthan from the first average value, wherein the remote device is mobilewhen the at least one x-position measurement deviates less from thefirst line of bets fit than from the average value; and determine whenthe at least one y-position measurement deviates less from the secondline of best fit than from the second average value, wherein the remotedevice is mobile when the at least one y-position measurement deviatesless from the first line of bets fit than from the average value.
 10. Anapparatus comprising: a transceiver, the transceiver being arranged toobtain estimated location information associated with a device; acategorization arrangement, the categorization arrangement beingconfigured to process the estimated location information to determinewhen the device is mobile, the categorization arrangement further beingconfigured to set at least one parameter based on whether the device ismobile, wherein the categorization arrangement is configured to set aprocess noise covariance to a first value and a measurement noisecovariance to a second value when the device is mobile, and to set theprocess noise covariance to a third value and the measurement noisecovariance to a fourth value when the device is not mobile; and afiltering arrangement, the filtering arrangement being configured to usethe at least one parameter to filter the estimated location information.11. An apparatus comprising: a transceiver, the transceiver beingarranged to obtain estimated location information associated with adevice; a categorization arrangement, the categorization arrangementbeing configured to process the estimated location information todetermine when the device is mobile, the categorization arrangementfurther being configured to set at least one parameter based on whetherthe device is mobile; and a filtering arrangement, the filteringarrangement being configured to use the at least one parameter to filterthe estimated location information, wherein the transceiver furtherincludes a radio frequency (RF) tag sensing device, the RF tag sensingdevice being arranged to obtain the estimated location information froman RF tag of the device.